Identify the Poi Visited Using the Tse Algorithm

The disclosure relates to the field of Intelligent Transportation Systems (ITS), and more specifically to a system and a method for identifying the point of interest (POI) visited using the trip start and end (TSE) algorithm.

Trip data, typically collected from various original equipment manufacturers (OEM), provide valuable insights into travel patterns and behaviors across various transportation modes. Analyzing this data can reveal critical aspects of travel behavior, including route choice, travel patterns, driver behavior, and traffic demand. However, the trip data is anonymized to protect user privacy by periodically truncating, thereby resulting in multiple trip IDs for a single origin-destination (OD) trip. This implies that a continuous trip is split into several segments, each segment has its unique trip ID. Further, there may be gaps between successive trip IDs due to truncation, thereby complicating tracking of the complete OD trip.

In general, the trip officially begins at the origin when a vehicle starts from a rest position and ends at the destination when the vehicle is parked back. However, if the trip ID changes near a point of interest (POI) other than the destination of the OD trip, then it becomes inconclusive whether it was only a trip ID rotation or whether the trip ended at the POI. Consequently, there is a need for a system and method that can efficiently collect and process trip data from vehicles to extract useful information while determining whether that trip ended at the POI or is just a continuation of the larger journey.

The present disclosure provides systems and methods for identifying the point of interest (POI) visited using the trip start and end (TSE) algorithm. Some example embodiments are directed towards analyzing trip data associated with a trip of a vehicle. The trip data may include information such as speed information, location information, orientation information, lane information, time information, or the like associated with one or more vehicles. According to some example embodiments, the trip data stored in the multi-purpose trip database may be utilized to determine the probability that a trip ended at the POI. In some embodiments, the trip data may be provided as dynamic content data to other service providers, while protecting personal identifiable information (PII) associated with probe data using which the trip data is generated. In an embodiment, the TSE algorithm is applied on the probe data and if the output of the TSE algorithm indicates that the trip ended near the POI, then the system further determines, using a machine learning algorithm, a stop event of the vehicle near the POI. This approach allows for the determination of a probability that the trip ended at the POI.

In one aspect, a system for identifying the POI using the TSE algorithm is provided. The system may include a memory configured to store computer-executable instructions, and at least one processor configured to execute the computer-executable instructions. The processor may be configured to receive trip data associated with each of a plurality of data points of a trip segment of a trip. The trip data includes speed data and location data. Further, the processor may be configured to determine a deceleration condition associated with a set of consecutive data points of the plurality of data points. The set of consecutive data points terminates at an end point of the plurality of data points. Further, in response to determining the deceleration condition, the processor may be configured to determine a geographical region associated with the set of consecutive data points based on the location data of each data point of the set of consecutive data points. The processor may be configured to determine the geographical region to be associated with a point of interest (POI) location. The processor may be further configured to determine, using a machine learning (ML) model, a probability value for the occurrence of a stop event of the trip based on the determined geographical region to correspond to the POI location. Furthermore, based on the determination of the probability value to be greater than a threshold value, the processor may be configured to store the trip data in association with the stop event of the trip.

In additional system embodiments, the trip data is associated with the trip of a vehicle, and the deceleration condition is associated with the deceleration of the vehicle.

In additional system embodiments, the processor may be further configured to identify the set of consecutive data points. The set of consecutive data points includes a pre-defined number of consecutive data points of the plurality of data points. Further, the processor may be further configured to determine a first value associated with the set of consecutive data points based on the speed data associated with each data point of the set of consecutive data points. The first value indicates one of an acceleration of the vehicle, or the deceleration of the vehicle.

In additional system embodiments, on determining the geographical region to be associated with the POI location, the processor may be further configured to determine a time period associated with the stop event of the vehicle within a vicinity of the POI location. Further, the processor may be further configured to compare the time period of the stop event with a time threshold. Thereafter, the processor is further configured to determine the POI location to be associated with a parking area based on the comparison.

In additional system embodiments, the processor may be further configured to obtain map data. The map data comprises a plurality of POI locations. Further, the processor may be further configured to compare the determined geographical region with each of the plurality of POI locations. Furthermore, the processor may be configured to determine the geographical region to be associated with the POI location of the plurality of POI locations based on the comparison.

In additional system embodiments, the trip segment is associated with a trip identifier.

In additional system embodiments, the trip data further includes lane information of the vehicle associated with each of a plurality of data points, and orientation information of the vehicle associated with each of a plurality of data points.

In additional system embodiments, the processor may be further configured to determine, using the ML model, the probability value for the occurrence of the stop event based on the lane information of the vehicle associated with each of the set of consecutive data points, and the orientation information of the vehicle associated with each of the set of consecutive data points.

In additional system embodiments, the processor may be further configured to receive historical data associated with the POI location. Further, the processor may be configured to determine vehicle data associated with the POI location based on historical data, The vehicle data is associated with one or more parked vehicles within a threshold distance from the POI location. Furthermore, the processor may be configured to determine, using the ML model, the probability value associated with the occurrence of the stop event of the trip based on the vehicle data.

In additional system embodiments, the processor may be configured to re-train the ML model based on the trip data, and historical data associated with the POI location and store the re-trained ML model.

In additional system embodiments, the processor may be further configured to determine POI information from map database. Further, the processor may be configured to generate, using the ML model, a label corresponding to the trip segment. The labels indicate the POI information corresponding to the stop event. The processor may be further configured to store the trip data in association with the labels.

In additional system embodiments, the ML model is trained on a historical trip data associated with one or more vehicles. The processor may be configured to train the ML model based on the historical trip data to determine the probability value for the occurrence of the stop event of the trip.

In another aspect, a method for identifying the POI using the TSE algorithm is provided. The method may include receiving trip data associated with each of a plurality of data points of a trip segment of a trip. The trip data includes speed data and location data. Further, the method may include determining a deceleration condition associated with a set of consecutive data points of the plurality of data points. The set of consecutive data points terminate at an end point of the plurality of data points. Further, in response to determining the deceleration condition, the method may include determining a geographical region associated with the set of consecutive data points based on the location data of each data point of the set of consecutive data points. The method may include determining the geographical region to be associated with a point of interest (POI) location. The method may include determining, using a machine learning (ML) model, a probability value for occurrence of a stop event of the trip based on the determined geographical region to correspond to the POI location. Furthermore, based on the determination of the probability value to be greater than a threshold value, the method may include storing the trip data in association with the stop event of the trip.

In additional method embodiments, the method may include identifying the set of consecutive data points. The set of consecutive data points includes a pre-defined number of consecutive data points of the plurality of data points. Further, the method may include determining a first value associated with the set of consecutive data points based on the speed data associated with each data point of the set of consecutive data points. The first value indicates one of an acceleration of the vehicle, or the deceleration of the vehicle.

In additional method embodiments, on determining the geographical region to be associated with the POI location, the method may include determining a time period associated with the stop event of the vehicle within a vicinity of the POI location. Further, the method may include comparing the time period of the stop event with a time threshold. Thereafter, the method may include determining the POI location to be associated with a parking area based on the comparison.

In additional method embodiments, the method may include obtaining map data. The map data comprises a plurality of POI locations. Further, the method may include comparing the determined geographical region with each of the plurality of POI locations. Furthermore, the method may include determining the geographical region to be associated with the POI location of the plurality of POI locations based on the comparison.

In additional method embodiments, the trip data further includes lane information of the vehicle associated with each of a plurality of data points, and orientation information of the vehicle associated with each of a plurality of data points.

In additional method embodiments, the ML model is trained on a historical trip data associated with one or more vehicles. The method may include training the ML model based on the historical trip data to determine the probability value for the occurrence of the stop event of the trip.

In yet another aspect, a computer programmable product is provided. The computer programmable product comprises a non-transitory computer readable medium having stored thereon computer executable instructions, which when executed by one or more processors, cause the one or more processors to conduct operations. The operations may include receiving trip data associated with each of a plurality of data points of a trip segment of a trip. The trip data includes speed data and location data. Further, the operations may include determining a deceleration condition associated with a set of consecutive data points of the plurality of data points. The set of consecutive data points terminate at an end point of the plurality of data points. Further, in response to determining the deceleration condition, the operations may include determining a geographical region associated with the set of consecutive data points based on the location data of each data point of the set of consecutive data points. The operations may include determining the geographical region to be associated with a point of interest (POI) location. The operations may include determining, using a machine learning (ML) model, a probability value for occurrence of a stop event of the trip based on the determined geographical region to correspond to the POI location. Furthermore, based on the determination of the probability value to be greater than a threshold value, the operations may include storing the trip data in association with the stop event of the trip.

In additional computer programmable product embodiments, the operations may include identifying the set of consecutive data points. The set of consecutive data points includes a pre-defined number of consecutive data points of the plurality of data points. Further, the operations may include determining a first value associated with the set of consecutive data points based on the speed data associated with each data point of the set of consecutive data points. The first value indicates one of an acceleration of the vehicle, or the deceleration of the vehicle.

With the ongoing industry segments, road traffic is sensed with the use of vision sensors, which do not cover the car's vision. To overcome this, some embodiments are directed towards using the car's vision to sense the road traffic. Some embodiments provide an ability to create a comprehensive suite of analytical insights that may refine the extraction of trip data from the probe data. The current invention provides an efficient architecture for extracting the trip data. The extracted trip data may be gathered in a timely manner and may be provided to other service providers. The trip data may be compatible with map technologies and may ensure at the same time that the privacy of the user from where the trip data was extracted is maintained.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, to one skilled in the art that the present disclosure may be practiced without these specific details. In other instances, systems and methods are shown in block diagram form only in order to avoid obscuring the present disclosure.

Some embodiments of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all, embodiments of the disclosure are shown. Indeed, various embodiments of the disclosure may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Like reference numerals refer to like elements throughout. Also, reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. The appearance of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Further, the terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but not for other embodiments. As used herein, the terms “data,” “content,” “information,” and similar terms may be used interchangeably to refer to data capable of being displayed, transmitted, received, and/or stored in accordance with embodiments of the present disclosure. Thus, use of any such terms should not be taken to limit the spirit and scope of embodiments of the present disclosure.

As defined herein, a “computer-readable storage medium,” which refers to a non-transitory physical storage medium (for example, volatile or non-volatile memory device), may be differentiated from a “computer-readable transmission medium,” which refers to an electromagnetic signal.

The embodiments are described herein for illustrative purposes. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient but are intended to cover the application or implementation without departing from the spirit or the scope of the present disclosure. Further, it is to be understood that the phraseology and terminology employed herein are for the purpose of the description and should not be regarded as limiting. Any heading utilized within this description is for convenience only and has no legal or limiting effect.

The present invention relates to a method and a system for identifying the POI using the TSE algorithm, using a machine learning model. Trip data associated with the trips may be received from one or more vendors of the trip data. The trip data may be stored in a well-organized and efficiently indexed trip database. Accordingly, various embodiments provide the method and the system to receive information related to the trip data from probe trajectory data or probe data. The probe data may be collected from vehicles, which may provide detailed information about travel patterns and trip dynamics. In another embodiment, the probe data may be received from one or more databases associated with one or more vendors of the probe data. The received trip data may be used to determine the deceleration of the vehicle, using the TSE algorithm. Trip data may be further used to determine the probability that the trip ended at the POI using a machine learning model. This may help to extract useful information to determine whether the trip ended at the POI, or is just a continuation of the larger journey. Furthermore, trip data may be used to update trip generation forecasts in real-time, reflecting changes in travel patterns and deceleration of the vehicle.

1 FIG. 100 102 102 104 108 106 102 110 104 112 114 100 illustrates a schematic diagram of a network environmentin which a systemfor identifying the POI using the TSE algorithm is implemented, in accordance with an embodiment of the disclosure. The systemmay be communicatively coupled to a database, and a mapping platformvia a communication network. Further, the systemmay include a machine learning (ML) model. the databasemay store trip dataassociated with each of a plurality of data pointsof a trip segment of a trip. The components described in the network environmentmay be further broken down into more than one component such as one or more sensors or applications in user equipment and/or combined together in any suitable arrangement. Further, one or more components may be rearranged, changed, added, and/or removed without deviating from the scope of the present disclosure.

102 102 112 114 112 112 104 102 114 102 102 102 110 112 The systemmay include suitable logic, circuitry, interfaces, and/or code that may be configured for identifying the POI using the TSE algorithm. In an embodiment, the systemmay be configured to receive the trip dataincluding the plurality of data points. The trip dataincludes speed data and location data. The trip datamay be received from the databaseor various original equipment manufacturers (OEM). The systemmay further determine a deceleration condition associated with a set of consecutive data points of the plurality of data points. The systemmay further determine a geographical region associated with the set of consecutive data points based on the location data of each data point of the set of consecutive data points, in response to determining the deceleration condition. The systemmay further determine the geographical region to be associated with a point of interest (POI) location. The systemmay further determine, using the machine learning (ML) model, a probability value for an occurrence of a stop event of the trip based on the determined geographical region to correspond to the POI location, and store the trip datain association with the stop event of the trip.

102 102 In an exemplary embodiment, the systemmay be embodied in one or more of several ways as per the required implementation. For example, the systemmay be embodied as a cloud-based service, a cloud-based application, a remote server-based service, a remote server-based application, a virtual computing system, a remote server platform, or a cloud-based platform.

102 110 102 110 110 112 114 110 108 110 110 The systemmay further include the ML model. The systemmay be further configured to determine the probability value for the occurrence of a stop event of the trip using the ML model. The ML modelmay be a classification model that may be trained to identify a relationship between inputs, (such as trip datain a training dataset that may include a dataset of the plurality of data points) and output probability value for the occurrence of a stop event. The ML modelmay be defined by its hyper-parameters, for example, the number of weights, cost function, input size, number of layers, and the like. The hyper-parameters of the ML modelmay be tuned and weights may be updated to move towards a global minima of a cost function for the ML model. After several epochs of training on the feature information in the training dataset, the neural network model may be trained to generate the ML modeland subsequently output a classification result for a set of inputs.

110 110 110 110 102 110 102 110 108 1 FIG. The ML modelmay include electronic data, such as, for example, a software program, code of the software program, libraries, applications, scripts, or other logic or instructions for execution by a processing device, such as circuitry. The ML modelmay be implemented using hardware including a processor, a microprocessor (e.g., to perform or control the performance of one or more operations), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). Alternatively, in some embodiments, the ML modelmay be implemented using a combination of hardware and software. Although in, the ML modelis shown integrated within the system, the disclosure is not so limited. Accordingly, in some embodiments, the ML modelmay be a separate entity in the system, without deviation from the scope of the disclosure. Examples of the ML modelmay include but are not limited to, a linear regression model, a logistic regression model, a decision tree model, a random forest-based model, a support vector machines (SVM) based model, and a K-mean-based model. In an embodiment, the ML modelmay correspond to a trained neural network model, such as, but not limited to an artificial neural network (ANN) based model, Artificial Neural Network Long Short-Term Memory network (ANN-LSTM), a fully connected neural network-based model, and/or a combination of such networks.

104 106 104 112 112 112 104 112 114 The databasemay be a trip database, but in alternate embodiments, the databasemay be embodied as a client-side map database and may represent a compiled trip data database that may be used in or with end user equipment such as a user device to provide trip data. The databasemay be configured to store the trip dataover a period of time. The trip datamay be collected by one or more devices such as one or more sensors or image capturing devices or mobile devices. In an embodiment, the trip datamay also be captured from connected-car sensors, smartphones, personal navigation devices, fixed road sensors, smart-enabled commercial vehicles, and expert monitors observing accidents and construction. In an embodiment, the databasemay be configured to store the trip dataincluding the plurality of data pointsof a trip segment of a trip.

108 108 108 108 108 108 108 108 108 108 108 108 The mapping platformmay include the map databaseB for storing map data and a processing serverA. The map databaseB may store node data, road segment data, link data, point of interest (POI) data, link identification information, heading value records, data about various geographic zones and regions, pedestrian data for different regions, heat maps, or the like. Also, the map databaseB further includes speed limit data of different lanes, cartographic data, routing data, and/or maneuvering data. Additionally, the map databaseB may be updated dynamically to accumulate real-time traffic data. The real-time traffic data may be collected by analyzing the location transmitted to the mapping platformby a large number of road users through the respective user devices of the road users. In one example, by calculating the speed of the road users along a length of the road, the mapping platformmay generate a live traffic map, which is stored in the map databaseB in the form of real-time traffic conditions. In an embodiment, the map databaseB may store data from different zones in a region. In one embodiment, the map databaseB may further store historical traffic data that includes travel times and average speeds on each road or area at any given time of the day and any day of the year. In an embodiment, the map data in the map databaseB may be in the form of map tiles. Each map tile may denote a map tile area including a plurality of road segments or links within the map tile.

108 108 108 108 108 108 108 According to some example embodiments, the road segment data records may be links or segments representing roads, streets, or paths, as may be used in calculating a route or recorded route information for the determination of one or more personalized routes. The node data may be ending points corresponding to the respective links or segments of road segment data. The road link data and the node data may represent a road network used by vehicles such as cars, trucks, buses, motorcycles, and/or other entities. Optionally, the map databaseB may contain path segment and node data records, such as shape points or other data that may represent pedestrian paths, links, or areas in addition to or instead of the vehicle road record data, for example. The road/link and nodes may be associated with attributes, such as geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and other navigation-related attributes. The map databaseB may also store data about the POIs and their respective locations in the POI records. The map databaseB may additionally store data about places, such as cities, towns, or other communities, and other geographic features such as bodies of water, mountain ranges, etc. Such place or feature data may be part of the POI data or may be associated with POIs or POI data records (such as a data point used for displaying or representing a position of a city). In addition, the map databaseB may include event data (e.g., traffic incidents, construction activities, scheduled events, unscheduled events, accidents, diversions, etc.) associated with the POI data records or other records of the map databaseB associated with the mapping platform. Optionally, the map databaseB may contain path segment records and node data records or other data that may represent pedestrian paths or areas in addition to or instead of the autonomous vehicle road record data.

108 108 108 108 As mentioned above, the map databaseB may be a master geographic database, but in alternate embodiments, the map databaseB may be embodied as a client-side map database and may represent a compiled navigation database that may be used in or with end user equipment such as the user device to provide navigation and/or map-related functions. For example, the map databaseB may be used with the user device to provide an end user with navigation features. In such a case, the map databaseB may be downloaded or stored locally (cached) on the user device.

108 108 108 108 108 The processing serverA may include processing means, and communication means. For example, the processing means may include one or more processors configured to process requests received from the user device. The processing means may fetch map data from the map databaseB and transmit the same to the user device. In one or more example embodiments, the mapping platformmay periodically communicate with the user device via the processing serverA to update a local cache of the map data stored on the user device. Accordingly, in some example embodiments, the map data may also be stored on the user device and may be updated based on periodic communication with the mapping platform.

102 106 108 In some example embodiments, the user device (not shown) may be any user accessible device such as a mobile phone, a smartphone, a portable computer, and the like, as a part of another portable/mobile object such as a vehicle. The user device may include a processor, a memory, and a communication interface. The processor, the memory, and the communication interface may be communicatively coupled to each other. In some example embodiments, the user device may be associated, coupled, or otherwise integrated with a vehicle of the user, such as an advanced driver assistance system (ADAS), a personal navigation device (PND), a portable navigation device, an infotainment system and/or other device that may be configured to provide route guidance and navigation-related functions to the user. In such example embodiments, the user device may include processing means such as a central processing unit (CPU), storage means such as on-board read only memory (ROM) and random access memory (RAM), acoustic sensors such as a microphone array, position sensors such as a GPS sensor, gyroscope, a LIDAR sensor, a proximity sensor, motion sensors such as accelerometer, a display enabled user interface such as a touch screen display, and other components as may be required for specific functionalities of the user device. Additional, different, or fewer components may be provided. In one embodiment, the user device may be directly or indirectly coupled to the systemvia the communication network. For example, the user device may be a dedicated vehicle (or a part thereof) for gathering data for the development of the map data in the map databaseB. In some example embodiments, the user device may serve the dual purpose of a data gatherer and a beneficiary device. The user device may be configured to capture sensor data associated with a road that the user device may be traversing. The sensor data may for example be image data of road objects, road signs, or the surroundings. The sensor data may refer to sensor data collected from a sensor unit in the user device.

106 106 108 102 102 108 106 The communication networkmay be wired, wireless, or any combination of wired and wireless communication networks, such as cellular, Wi-Fi, internet, local area networks, or the like. In one embodiment, the communication networkmay include one or more networks such as a data network, a wireless network, a telephone network, or any combination thereof. It is contemplated that the data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), a public data network (e.g., the Internet), short range wireless network, or any other suitable packet-switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiber-optic network, and the like, or any combination thereof. In addition, the wireless network may be, for example, a cellular network and may employ various technologies including enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., worldwide interoperability for microwave access (WiMAX), Long Term Evolution (LTE) networks (e.g. LTE-Advanced Pro), 5G New Radio networks, ITU-IMT 2020 networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (Wi-Fi), wireless LAN (WLAN), Bluetooth, Internet Protocol (IP) data casting, satellite, mobile ad-hoc network (MANET), and the like, or any combination thereof. For example, the mapping platformmay be integrated into a single platform to provide a suite of mapping and navigation-related applications for OEM devices, such as the user devices and the system. The systemmay be configured to communicate with the mapping platformover the communication network.

102 112 112 114 112 112 104 112 3 FIG.A 5 FIG. In operation, the systemis configured to receive the trip data. The trip datamay include the plurality of data pointsof the trip segment of the trip. The trip dataincludes speed data and location data. In an embodiment, the trip datamay be stored in the database. Details about the received trip dataare provided, for example, inand.

102 114 114 102 3 FIG.A In an embodiment, the systemis configured to determine a deceleration condition associated with a set of consecutive data points of the plurality of data points. The set of consecutive data points terminates at an end point of the plurality of data points. Further, in response to determining the deceleration condition, the systemmay be configured to determine a geographical region associated with the set of consecutive data points based on the location data of each data point of the set of consecutive data points. Details associated with the determined deceleration condition are provided, for example, in.

102 102 110 102 3 FIG.B In an embodiment, the systemis configured to determine the geographical region to be associated with a point of interest (POI) location. The systemmay be further configured to determine, using a machine learning (ML) model, a probability value for the occurrence of a stop event of the trip based on the determined geographical region to correspond to the POI location. Furthermore, based on the determination of the probability value to be greater than a threshold value, the systemmay be configured to store the trip data in association with the stop event of the trip. Details associated with the determination of the probability value are provided, for example, in.

2 FIG. 1 FIG. 102 202 202 204 204 206 206 208 208 202 202 202 202 202 202 204 204 112 110 illustrates a block diagram of the system of, in accordance with an embodiment of the disclosure. The systemmay include at least one processor(hereinafter, also referred to as “processor”), at least one memory(hereinafter, also referred to as “memory”), I/O interface(hereinafter, also referred to as “I/O interface”), and communication interface(hereinafter, also referred to as “communication interface”). The processormay include an input moduleA, a machine learning application moduleB, a deceleration condition determination moduleC, and an output moduleD. The processormay retrieve computer program code instructions that may be stored in the memoryfor execution of the computer program code instructions. The memorymay store data including the trip data, and the ML model.

202 202 202 202 202 202 204 102 The processormay be embodied in a number of different ways. For example, the processormay be embodied as one or more of various hardware processing means such as a coprocessor, a microprocessor, a controller, a digital signal processor (DSP), a processing element with or without an accompanying DSP, or various other processing circuitry including integrated circuits such as an ASIC (application specific integrated circuit), an FPGA (field programmable gate array), a microcontroller unit (MCU), a hardware accelerator, a special-purpose computer chip, or the like. As such, in some embodiments, the processormay include one or more processing cores configured to perform independently. A multi-core processor may enable multiprocessing within a single physical package. Additionally, or alternatively, the processormay include one or more processors configured in tandem via the bus to enable independent execution of instructions, pipelining, and/or multithreading. Additionally, or alternatively, the processormay include one or more processors capable of processing large volumes of workloads and operations to provide support for big data analysis. In an example embodiment, the processormay be in communication with the memoryvia a bus for passing information among components of the system.

202 202 202 202 202 202 100 208 102 208 102 For example, when the processormay be embodied as an executor of computer program code instructions, the instructions may specifically configure the processorto perform the algorithms and/or operations described herein when the instructions are executed. However, in some cases, the processormay be a processor-specific device (for example, a mobile terminal or a fixed computing device) configured to employ an embodiment of the present disclosure by further configuration of the processorby instructions for performing the algorithms and/or operations described herein. The processormay include, among other things, a clock, an arithmetic logic unit (ALU), and logic gates configured to support the operation of the processor. The network environment, such as the network environmentmay be accessed using the communication interfaceof the system. The communication interfacemay provide an interface for accessing various features and data stored in the system.

202 202 112 114 202 112 112 112 112 112 112 102 208 208 102 In an embodiment, the input moduleA of the processormay receive trip dataincluding the plurality of data pointsThe input moduleA may receive the trip datafrom one or more sensors including but not limited to acoustic sensors such as a microphone array, position sensors such as a GPS sensor, a gyroscope, motion sensors such as accelerometer, an image sensor such as a camera and the like. The trip datamay include speed data and location data. The trip datafurther includes lane information of a vehicle associated with each of a plurality of data points and orientation information of the vehicle associated with each of a plurality of data points. The received trip datafrom the trips may provide a relevant aspect of the trips to be used in a plurality of aspects such as route recommendations, improved estimation of arrival times, optimized routes for navigation, multi-modal transportation, or the like. The trip dataextracted from trips may be used in product use cases. The extracted trip data may be defined, generated, and stored in a manner that the privacy of the user may be protected. The user may be associated with the trip from where the trip datais received. The systemmay be accessed using the communication interface. The communication interfacemay provide an interface for accessing various features and data stored in the system.

202 202 102 112 114 102 112 Furthermore, in another embodiment, the input moduleA of the processormay further be configured to execute the computer program code instructions which may be configured to cause the systemto receive the trip dataincluding the plurality of data points. In an example, the systemmay output the probability value for the occurrence of a stop event of the trip based on the trip data, via a user interface.

202 202 110 202 202 110 202 110 112 114 The training moduleB of the processormay be configured to train the ML modelto generate the probability value. In an embodiment, the training moduleB of the processormay be configured to re-train the ML modelin certain iterations to improve accuracy of the generated probability value. In an embodiment, the training moduleB trains the ML modelto employ ML algorithms and techniques to analyze the trip dataassociated with each datapoint of the plurality of datapoints, and further generate the probability value based on the analysis.

202 202 202 202 The labelling moduleC of the processormay be configured to associate each stop event of the one or more stop events with a type of event label. In particular, the labelling moduleC may be configured to determine whether the reference location associated with a stop event of the one or more stop events corresponds to the signalized intersections, the non-signalized intersection, the road segment, or the POI. Further, the labelling moduleC may be configured to associate the stop event with the type of event label based on the determination that the reference location of the stop event corresponds to one of the signalized intersections, the non-signalized intersection, or the road segment between road segments.

202 102 112 102 110 112 108 The output moduleD may be configured to output the probability value for the occurrence of a stop event of the trip. In an embodiment, the systemmay be configured to output the probability value for the occurrence of the stop event of the trip of the vehicle. For example, the trip datamay include the speed data and location data to determine the deceleration of the vehicle in the vicinity of the POI. In an embodiment, in response to the deceleration of the vehicle near the POI, the systemmay be configured to leverage the use of ML modelto determine whether that trip ended at the POI or is just a continuation of the larger journey. The trip datamay also include tile level data obtained from the map databaseB.

204 102 112 110 112 112 204 204 202 204 102 The memoryof the systemmay be configured to store the trip data, the ML model. The trip datamay include speed data, location data, lane information of the vehicle associated with each of a plurality of data points, and orientation information of the vehicle associated with each of a plurality of data points. In an example, the trip dataat least one of a trip origin timestamp, a trip transportation mode, a trip origin city, a trip travel time of the vehicle, a trip tile ID associated with the origin and destination of the trip, a trip start latitude, a trip start longitude, a trip end latitude, a trip end longitude, a trip probe frequency of the probe, a trip dwell count of the vehicle, a trip probe data type. The memorymay be non-transitory and may include, for example, one or more volatile and/or non-volatile memories. In other words, for example, the memorymay be an electronic storage device (for example, a computer readable storage medium) including gates configured to store data (for example, bits) that may be retrievable by a machine (for example, a computing device like the processor). The memorymay be configured to store information, data, content, applications, instructions, or the like, for enabling the systemto carry out various functions in accordance with an example embodiment of the present disclosure.

204 202 204 202 202 202 202 2 FIG. For example, the memorymay be configured to buffer input data for processing by the processor. As exemplarily illustrated in, the memorymay be configured to store instructions for execution by the processor. As such, whether configured by hardware or software methods, or by a combination thereof, the processormay represent an entity (for example, physically embodied in circuitry) capable of performing operations according to an embodiment of the present disclosure while configured accordingly. Thus, for example, when the processoris embodied as an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or the like, the processormay be specifically configured hardware for conducting the operations described herein.

206 102 206 102 102 206 102 206 102 102 202 206 204 202 202 104 In some example embodiments, the I/O interfacemay be configured to receive the input and/or output generated by the system. In an embodiment, the I/O interfacemay be configured to communicate with the systemand display the input and/or output of the system. As such, the I/O interface(for example, an infotainment system) may include a display screen and, in some embodiments, may also include a keyboard, a mouse, a joystick, a touch screen, touch areas, soft keys, one or more microphones, a plurality of speakers, or other input/output mechanisms. In one embodiment, the systemmay include a user interface circuitry configured to control at least some functions of one or more I/O interface elements such as a display device and, in some embodiments, a plurality of speakers, a ringer, one or more microphones and/or the like. In an embodiment, the I/O interfacemay include an input interface and output interface for supporting communications to and from the systemor any other component with which the systemmay communicate. The processormay be configured to control one or more functions of one or more I/O interfaceelements through computer program instructions (for example, software and/or firmware) stored on the memoryaccessible to the processor. The processormay further render the first trip data associated with the identified first trip of the vehiclevia the user interface or the I/O interface.

208 102 208 208 208 208 208 The communication interfacemay be any means such as a device or circuitry embodied in either hardware or a combination of hardware and software that is configured to receive and/or transmit data to/from other communication devices in communication with the system. In this regard, the communication interfacemay include, for example, one or more antennas and supporting hardware and/or software for enabling communications with a wireless communication network. Additionally, or alternatively, the communication interfacemay include the circuitry for interacting with the antenna(s) to cause transmission of signals via the antenna(s) or to handle receipt of signals received via the antenna(s). In some environments, the communication interfacemay alternatively or additionally support wired communication. As such, for example, the communication interfacemay include a communication port, a communication modem and/or other hardware and/or software for supporting communication via cable, digital subscriber line (DSL), universal serial bus (USB), or other mechanisms. In some embodiments, the communication interfacemay enable communication with a cloud-based network to enable deep learning.

102 102 102 In an embodiment, the systemmay be configured to receive the trip data associated with each of a plurality of data points of a trip segment of a trip. In an embodiment, the deceleration condition associated with a set of consecutive data points of the plurality of data points may be determined. Further, based the determined deceleration condition, the systemmay be configured to determine a geographical region associated with the set of consecutive data points based on the location data of each data point of the set of consecutive data points. In an exemplary embodiment, the geographical region may be associated with a point of interest (POI) location. The systemmay be configured to determine the probability value for occurrence of the stop event of the trip based on the determined geographical region to correspond to the POI location.

112 108 108 108 Further, in another embodiment, the trip datamay be stored by the map databaseB of the mapping platform. The mapping platformmay use the trip data to enhance traffic analytics by exploring various applications such as origin-destination (OD) estimation, route recommendations, improved estimated times of arrival (ETAs), venue analytics, optimized routing for electric vehicles (EVs), and multi-modal transportation.

102 108 110 102 104 108 110 In an embodiment, the systemmay output the trip data to the mapping platformvia the communication network. To that end, the systemmay be communicatively coupled to the databaseand the mapping platformvia the communication network.

3 FIG.A 3 FIG.B 3 FIG.A 3 FIG.B 1 FIG. 2 FIG. 3 FIG.A 3 FIG.B 1 FIG. 2 FIG. 300 302 328 300 102 202 300 andjointly illustrate a flowchart of a method for determining a probability value for an occurrence of a stop event, in accordance with an embodiment of the disclosure.andare explained in conjunction with elements from, and. With reference toand, there is shown the block diagramthat illustrates exemplary operations fromto, as described herein. The exemplary operations illustrated in the block diagrammay be performed by any computing system, apparatus, or device, such as by the systemofor the processorof. Although illustrated with discrete blocks, the exemplary operations associated with one or more blocks of the block diagrammay be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the implementation.

302 112 202 112 114 At, the trip datamay be received. In an embodiment, the processormay be configured to receive the trip dataassociated with each of the plurality of data pointsof a trip segment of a trip. The trip refers to a trip of a vehicle from one location to another (such as from an origin to a destination). For example, a user of the vehicle may take an origin-destination (OD) trip for various purposes, such as leisure, business, personal reasons, and the like. For example, the OD trip may include multiple destinations or activities. In such an example, the trip may be divided into multiple trip segments, where each trip segment may correspond to a short trip. Further, the trip segment may be associated with a trip identifier. For example, each segment has its unique trip identifier. Such trip identifiers may be truncated to provide anonymity and protect user privacy. For example, the OD trip may be divided into multiple trip segments, and each of the trip segments may be associated with a trip identifier. This may imply that there may be multiple trip identifiers for a single OD trip. For example, the OD trip may include 5 trip segments corresponding to Trip ID 1, trip ID 2, trip ID 3, trip ID 4, and Trip ID 5.

114 Further, each trip segment may include the plurality of data pointssuch as but not limited to a first data point, a second data point, a third data point, and an Nth data point. Each data point is a record of a specific location of the vehicle at a specific timestamp. The data point may include the specific time stamp, and a set of geographical coordinates (latitude and longitude) associated with the vehicle. The data point may include the speed of the vehicle, and a heading value associated with the vehicle. The speed of the vehicle may correspond to the velocity of the vehicle at that specific point in time. The heading value may be indicative of a direction in which the vehicle may be moving and is typically measured in degrees relative to true north (0° to 360°).

112 In an embodiment, the trip datamay be associated with the trip of the vehicle. The vehicle may be a non-autonomous vehicle, a semi-autonomous vehicle, or a fully autonomous vehicle, for example, as defined by the National Highway Traffic Safety Administration (NHTSA). Examples of the vehicle may include but are not limited to, a two-wheeler electric vehicle, a three-wheeler electric vehicle, a four-wheeler electric vehicle, or more than a four-wheeler electric vehicle. Examples of two-wheeler vehicles may include, but are not limited to, an electric two-wheeler, or a hybrid two-wheeler. Similarly, examples of the four-wheeler vehicle may include, but are not limited to, an electric car, or a hybrid car. The present disclosure may also apply to other structures, designs, or shapes of the vehicle. The description of other types of vehicles and respective structures, designs, or shapes has been omitted from the disclosure for the sake of brevity.

In some example embodiments, the vehicle may include processing means such as a central processing unit (CPU), storage means such as on-board read-only memory (ROM), and random access memory (RAM), acoustic sensors such as a microphone array, position sensors such as a global positioning system (GPS) sensor, gyroscope, a light detection and ranging (LiDAR) sensor, a proximity sensor, motion sensors such as an accelerometer, an image sensor such as a camera, a display enabled user interface such as a touch screen display, and other components as may be required for specific functionalities of the vehicle. In some example embodiments, one or more user equipment may be associated, coupled, or otherwise integrated with the vehicle, such as an advanced driver assistance system (ADAS), a personal navigation device (PND), a portable navigation device, and/or other devices that may be configured to provide route guidance and navigation-related functions to a user.

112 112 114 112 The trip dataincludes speed data and location data. The speed data may correspond to information associated with the speed of the vehicle for the corresponding trip segment. The location data may correspond to information associated with the geographical region associated with the vehicle for the corresponding trip segment. For example, the received trip dataassociated with each of the plurality of data pointsmay include speed values for the vehicle at the corresponding data point, the location data of the vehicle at the corresponding data point, and the timestamp associated with the corresponding datapoint. The timestamp associated with the corresponding datapoint may indicate a specific time at which the trip datamay be collected or recorded at the location specified in the location data. The timestamp may provide temporal context to the data associated with the datapoint, allowing for analysis of traffic conditions, and speed variations over time.

304 202 114 114 At, the set of consecutive data points may be identified. In an embodiment, the processormay be configured to identify the set of consecutive data points. The set of consecutive data points may correspond to sequential data points associated with at least one of the start of the trip or the end of the trip. Further, the set of consecutive data points includes a pre-defined number of consecutive data points of the plurality of data points. Such a pre-defined number may be defined by the service provider (or OEMs). For example, of the pre-defined number of consecutive data points of the plurality of data pointsmay include but not be limited to numeric values, such as 5, 8, 10, 15, and the like.

306 202 114 At, a first value may be determined. In an embodiment, the processormay be configured to determine the first value associated with the set of consecutive data points based on the speed data associated with each data point of the set of consecutive data points. The first value indicates one of the acceleration of the vehicle or the deceleration of the vehicle. For example, the set of consecutive data points may correspond to sequential data points associated with an origin or start of the trip segment. The start of the trip segment may indicate the acceleration of the vehicle, where the speed value of the vehicle may increase from zero. Alternatively, the set of consecutive data points may correspond to sequential data points associated with a destination or end of the trip segment. The end of the trip segment may indicate the deceleration of the vehicle, where the speed value of the vehicle may approach zero. For example, the set of consecutive data points includes 10 consecutive data points of the plurality of data pointssampled over a time period of 3 minutes. Given, that the set of consecutive data points may correspond to 10 sequential data points associated with the destination or end of the trip segment, such as D1, D2, D3, . . . , D10. Each data point may include a speed value of the vehicle for the corresponding data points. For example, the first value may be calculated using the following equation:

First value=(10*SSD+100)/(SS+10)

Here, SSD refers to the sum of speed differences, and SS refers to the sum of speed metrics.

The SS may be calculated based on a summation of all the speed values from D1, D2, D3, . . . , and D10, and SSD may be calculated based on a summation of the difference of every two consecutive speed values. Thereafter, the first value may be calculated. For example, the first value may correspond to a numeric value between a range of −10 to +10.

308 310 312 At, determine whether the first value is less than or equal to zero. In an embodiment, the processor may be configured to compare the determined first value with the zero value. For example, when the first value is greater than zero, the control may pass to. On the contrary, when the first value is less than or equal to zero, the control may pass to.

310 202 At, the acceleration of the vehicle may be determined. In an embodiment, the processormay be configured to determine the acceleration of the vehicle based on the determination of the first value greater than zero. This may indicate that the vehicle has started moving from the origin point.

312 202 202 At, the deceleration of the vehicle may be determined. In an embodiment, the processormay be configured to determine the deceleration of the vehicle based on the determination that the first value is less than zero. This may indicate that the vehicle is decelerating at the end point and is about to stop. In an example, the processormay be configured to determine the deceleration of the vehicle based on the determination that the first value is equal to zero. This may indicate that the vehicle has stopped at the end point and a stop event is determined.

314 202 114 114 At, a deceleration condition may be determined. In an embodiment, the processormay be configured to determine the deceleration condition associated with a set of consecutive data points of the plurality of data points. The set of consecutive data points terminates at an endpoint of the plurality of data points. Further, the deceleration condition is associated with the deceleration of the vehicle. The deceleration condition may refer to a set of specifications indicative of the decrease in speed values of the vehicle over time. For example, a user applies a brake, thereby reducing the speed values of the vehicle until it stops or reaches a slower speed.

316 202 At, a geographical region may be determined. In an embodiment, in response to determining the deceleration condition, the processormay be configured to determine the geographical region associated with the set of consecutive data points based on the location data of each data point of the set of consecutive data points. The geographical region may refer to a set of geographical coordinates (latitude and longitude) associated with the vehicle when the vehicle is in the deceleration condition.

318 202 102 108 108 108 1 FIG. 8 FIG. 9 FIG. 10 FIG. At, map data may be obtained. In an embodiment, the processormay be configured to obtain map data. The map data may include the geographical region associated with the set of consecutive data points. For example, the map data may include information associated with a plurality of POI locations. In an embodiment, the systemmay be configured to obtain the map data from the map databaseB. The details about the map databaseB are provided in conjunction with, for example,,,, and. In one exemplary embodiment, the map data may be retrieved from a source other than the map databaseB. In an example, the information associated with the geographical region may include a latitude and a longitude position (e.g. 39.° N, 77° W) of each POI location of the plurality of POI locations within the geographical region. Similarly, each POI location of the plurality of POI locations may be associated with the corresponding information, such as type (e.g., office building, museum, restaurant, hotel, school, etc.), operating hours, etc. In addition to the latitude and longitude position, the information may also include information indicative of a location associated with the latitude and longitude position. For example, the latitude and longitude position as “39.° N, 77° W” may be associated with a location indicative of “Washington, D.C”. In one embodiment, latitude and longitude positions may be associated with their respective locations based on map-matching techniques, thereby forming the information associated with the plurality of POI locations within the geographical region.

The map matching techniques may include but are not limited to, a geometric analysis, a hidden Markov model, a topological relationship, a fuzzy logic model, D-S evidence theory, and a Bayesian inference. The map matching techniques may align a sequence of GPS co-ordinates (e.g. the latitude and longitude positions) with a corresponding road network. The goal of the map matching techniques is to reconstruct and smooth the trajectory by matching each GPS co-ordinate to the closest road segment on a digital road network.

320 202 102 102 At, the POI location may be determined. In an embodiment, the processormay be configured to determine the POI location based on the obtained map data. The POI location may include a latitude and longitude position, and a corresponding location associated with the latitude and longitude position. For example, based on a latitude and longitude position, ‘32° 08′59.96° N, 110° 50′09.03° W’, of the center location data, a corresponding location, for example, ‘Tucson, Arizona, United States of America (USA)’, is associated. Further, the information retrieved from the map data also includes a latitude and a longitude position and a corresponding location associated with each POI location of the plurality of POI locations. Further, to associate the determined geographical region with each of the plurality of POI locations, the systemmay be configured to compare the determined geographical region with each of the plurality of POI locations in the map data. For example, considering the location is matched to a location of a POI in the map data based on the comparison, the systemmay be configured to associate the determined geographical region with each of the plurality of POI locations.

322 202 202 At, a geographical region may be determined. In an embodiment, the processormay be configured to determine the geographical region to be associated with a point of interest (POI) location. Further, on determining the geographical region to be associated with the POI location, the processormay be configured to determine a time period associated with the stop event of the vehicle within the vicinity of the POI location. The time period associated with the stop event of the vehicle may be determined using the timestamp data of each data point of the set of consecutive data points. The time period may correspond to a numeric value for example, such as, but not limited to 2 minutes, 8 minutes, 15 minutes, 21 minutes, 24 minutes, and the like.

202 202 202 202 Thereafter, the processormay be configured to compare the time period of the stop event with a time threshold. The time threshold may correspond to a numeric value for example, such as, but not limited to 15 minutes. Based on the comparison, the processormay be configured to determine the POI location to be associated with a parking area. For example, in response to the determination of the time period of the stop event greater than or equal to the time threshold, then the processormay be configured to determine the POI location to be associated with the parking area. Alternatively, in response to the determination of the time period of the stop event less than the time threshold, then the processormay be configured to determine the POI location to be associated with a normal halt in the trip rather that the parking or the stop event.

324 202 110 At, a probability value may be determined. In an embodiment, the processormay be configured to determine, using the machine learning (ML) model, the probability value for the occurrence of the stop event of the trip based on the determined geographical region to correspond to the POI location. The probability value may indicate a likelihood that the vehicle made a stop at the POI location for the corresponding trip segment.

326 202 328 At, the probability value may be compared with a threshold value. In an embodiment, the processormay be configured to compare the probability value with the threshold value to determine whether the trip ended at the POI location or is just a continuation of the larger journey. For example, if the probability value is greater than the threshold value, the control may pass to. On the contrary, if the probability value is less than the threshold value, the control may pass to the end.

328 112 202 112 112 106 At, the trip datain association with the stop event of the trip may be stored. In an embodiment, the processormay be configured to store the trip datain association with the stop event of the trip based on the determination of the probability value to be greater than the threshold value. In an example, the trip datamay be stored in the database.

112 114 114 114 202 108 In an embodiment, the trip datamay include lane information of the vehicle associated with each of the plurality of data points, and orientation information of the vehicle associated with each of the plurality of data points. The lane information of the vehicle associated with each of the plurality of data pointsmay correspond to information associated with lane change by the user of the vehicle. For example, the vehicle may deviate from the road lanes towards the edge of the road or a designated parking area. The processormay be configured to perform lane-level map-matching using the map databaseB, to determine if the vehicle is parked in the designated parking area or not.

114 104 202 202 Further, the orientation information of the vehicle associated with each of the plurality of data pointsmay correspond to information associated with the change of the heading degree of the vehicle indicative of a direction in which the vehiclemay be moving and is typically measured in degrees relative to true north (0° to 360°). The processormay be configured to determine the heading degree associated with the orientation information of the vehicle with a threshold degree. For example, when the heading degree associated with the orientation information of the vehicle is greater than the threshold degree (such as 30 degrees), the processormay determine that the vehicle is parked near the POI location.

202 110 114 114 202 112 110 In an embodiment, the processormay be configured to determine, using the ML model, the probability value for the occurrence of the stop event based on the lane information of the vehicle associated with each of the set of consecutive data points, and the orientation information of the vehicle associated with each of the set of consecutive data points. For example, if the first value indicates that the trip has ended near the POI location, and there may be a change of the trip identifier near the POI location, then the processormay be configured to determine whether the trip ended at the POI location or is just a continuation of the larger journey. To determine the trip end, the trip datamay be analyzed, and based on the analysis the ML modelmay determine the probability value for the occurrence of the stop event of the trip.

202 110 In an embodiment, the processormay be configured to train the ML modelon historical data associated with one or more vehicles to determine the probability value for the occurrence of the stop event of the trip. The historical data associated with the POI location may correspond to past trip data associated with the POI location. The historical data associated with one or more vehicles may include speed data associated with each of the historical trips, location data associated with each of the historical trips, timestamp information associated with each of the historical trips, parking information associated with each of the historical trips, orientation information associated with the each of the historical trips, vehicle data associated with the vehicle, or a combination thereof.

202 202 In an embodiment, the processormay be configured to receive the historical data associated with the POI location. Further, the processormay be configured to determine vehicle data associated with the POI location based on historical data. The vehicle data is associated with one or more parked vehicles within a threshold distance from the POI location. For example, the vehicle data may indicate a number of vehicles parked at a particular parking area within the vicinity of the POI location in past days. For example, 12 cars may have been parked at the paring location in the past 3 days. Further, the vehicle data may include, for example, but is not limited to, the health status of the vehicle and one or more parameters associated with one or more electronic devices associated with the vehicle. The one or more electronic devices associated with the vehicle may include but are not limited to, a Heating, Ventilation, and Air Conditioning system, an infotainment system, an on-board diagnostics system, a Tire Pressure Monitoring System, a Battery Management System, a vehicle control unit, a navigation system, and an Advanced Driver Assistance System.

202 110 102 102 110 Further, the processormay be configured to determine, using the ML model, the probability value associated with the occurrence of the stop event of the trip based on the vehicle data. This may facilitate the systemto determine whether the vehicle is parked in association with the parking area associated with the POI location or is just an accidental stop. Therefore, the systemmay be configured to determine the end of the trip at the POI location using the ML model.

202 202 108 202 110 202 202 202 In an embodiment, the processormay be configured to associate each stop event with a label including the trip label based on the corresponding POI location. In an embodiment, the processormay be configured to determine POI information from map databaseB. Further, the processormay be configured to generate, using the ML model, a label corresponding to the trip segment. The label may indicate the POI information corresponding to the stop event. Thereafter, the processormay be configured to store the trip data in association with the label. For example, if the trip ended at a mall, then the processormay be configured to generate the label as a shopping trip. Similarly, if the trip ends near a coffee shop then the processormay be configured to generate the label a coffee trip.

4 FIG. 4 FIG. 1 FIG. 2 FIG. 3 FIG.A 3 FIG.B 4 FIG. 1 FIG. 2 FIG. 400 402 414 400 102 202 400 illustrates exemplary operations for determining the probability value for the occurrence of the stop event of the trip using the machine learning model, in accordance with an embodiment of the disclosure.is explained in conjunction with elements from,,, and. With reference to, there is shown the block diagramthat illustrates exemplary operations fromto, as described herein. The exemplary operations illustrated in the block diagrammay be performed by any computing system, apparatus, or device, such as by the systemofor the processorof. Although illustrated with discrete blocks, the exemplary operations associated with one or more blocks of the block diagrammay be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the implementation.

202 110 112 402 110 110 112 402 404 110 112 402 110 112 402 In an embodiment, the processormay be configured to re-train the ML modelbased on the trip data, and the historical dataassociated with the POI location and store the re-trained ML model. For example, the ML modelmay receive the trip data, and the historical dataas an input, and generate the probability valueas an output based on the received input. For example, the stop event occurs near a coffee shop or within the vicinity of the coffee shop, where the coffee shop is the POI location. The ML modelmay be trained to determine the probability value associated with the stop event and generate the label for the trip based on the trip data, and the historical data. Further, if the stop event occurs near more than one POI location, then the ML modelmay be configured to determine the probability value for each of the POI locations being visited individually, based on the trip data, and the historical data.

5 FIG. 5 FIG. 1 FIG. 2 FIG. 3 FIG. 4 FIG. 5 FIG. 500 500 502 500 504 504 504 504 504 504 504 504 504 504 202 112 illustrates a schematic diagram depicting a plurality of data points of a trip segment of a trip, in accordance with an embodiment of the disclosure.is explained in conjunction with elements from,,, and. With reference to, there is shown an exemplary diagramdepicting routeassociated with the trip of the vehicle upon the map interface. The routemay be considered as a journey that includes one or more data points for example, but not limited toA,B,C,D,E,F,G,H,I, andJ. The processormay be configured to receive the trip dataassociated with each of the plurality of data points of a trip segment of the trip.

504 112 504 112 506 504 504 506 504 102 504 504 504 106 110 In an exemplary embodiment, the first data pointA may be associated with the trip dataindicative of a starting point of the OD trip. The first data pointA may mark the beginning of a travel path of the trip. The travel path may be included in the trip data. For example, a vehicletravels between the first data pointA and the second data pointB, and the deceleration of the vehicleoccurs at the second data pointB. Further, the systemmay determine a stop event of the vehicle within the vicinity of the POI location at the second data pointB, for example, such as for 15 minutes, if the time period of the stop event is greater than or equal to a time threshold, then determine the POI location to be associated with the parking area. In such an example, the first data pointA and the second data pointB may be considered as the trip segment and stored in the database. Thereafter, in response to the determining the deceleration of the vehicle near the POI, and change of the trip identifier, the probability value for the occurrence of the stop event of the trip may be determined using the ML model, to determine whether the trip ended at the POI, or is a continuation of the larger journey.

506 504 504 504 102 506 504 504 504 In an exemplary embodiment, the vehicletravels between second data pointB and a third data pointC, and the deceleration of the vehicle occurs at the third probe pointC. Further, the systemmay determine a stop event of the vehiclewithin geographical region associated with the POI at the third data pointC, for example, such as for 5 minutes, if the time period of the stop event is less than the time threshold to then the POI location may not be associated with the parking area. In such an example, the second data pointB and the third data pointC may not be considered as the trip segment.

506 504 504 504 102 506 504 504 504 In an exemplary embodiment, the vehicletravels between third data pointC and a fourth data pointD, and the deceleration of the vehicle occurs at the fourth data pointD. Further, the systemmay determine a stop event of the vehiclewithin geographical region not associated with the POI location at the fourth data pointD. In such an example, the third data pointC and the fourth data pointD may not be considered as the trip segment.

6 FIG. 6 FIG. 1 FIG. 2 FIG. 3 FIG.A 3 FIG.B 4 FIG. 5 FIG. 6 FIG. 600 602 604 606 illustrates a schematic diagram of an exemplary POI, and a footprint associated therewith, in accordance with an embodiment of the disclosure.is explained in conjunction with elements from,,,,and. With reference to, there is shown the schematic diagramof the geographical regions associated with the POI location. There is shown a first geographical region, a second geographical region, and a third geographical region. Each geographical region includes one or more POI locations associated therewith.

602 602 602 602 602 602 602 604 604 606 606 102 For example, the first geographical regionincludes the one or more POI locationsA,B, andC. The POI locationA may correspond to a restaurant, the POI locationB may correspond to a shopping center, and the POI locationC may correspond to a commercial mall. Further, the second geographical locationmay include a POI locationA which may be a coffee place, and the third geographical locationmay include a POI locationA that may be a gaming zone. For example, the systemmay leverage the use of a bounding box around the POI location for geospatial analysis, thereby focusing on a specific location within a larger geographical region for detailed analysis or highlighting the POI location on a map.

7 FIG. 7 FIG. 1 FIG. 2 FIG. 3 FIG.A 3 FIG.B 4 FIG. 5 FIG. 6 FIG. 700 204 102 202 illustrates a flowchart of a method for identifying the POI using the TSE algorithm, in accordance with an embodiment of the disclosure.is explained in conjunction with elements from,,,,,, and. It will be understood that each block of the flow diagram of the flowchartmay be implemented by various means, such as hardware, firmware, processor, circuitry, and/or other communication devices associated with execution of software including one or more computer program instructions. For example, one or more of the procedures previously stated may be embodied by computer program instructions. In this regard, the computer program instructions that embody the procedures previously stated may be stored by a memoryof the system, employing an embodiment of the present invention and executed by a processor. As will be appreciated, any such computer program instructions may be loaded onto a computer or other programmable apparatus (for example, hardware) to produce a machine, such that the resulting computer or other programmable apparatus implements the functions specified in the flow diagram blocks. These computer program instructions may also be stored in a computer-readable memory that may direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture the execution of which implements the function specified in the flowchart blocks. The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operations to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions that execute on the computer or other programmable apparatus provide operations for implementing the functions specified in the flow diagram blocks.

700 7 FIG. Accordingly, blocks of the flow diagram support combinations of means for performing the specified functions and combinations of operations for performing the specified functions. It will also be understood that one or more blocks of the flow diagram, and combinations of blocks in the flow diagram, may be implemented by special-purpose hardware-based computer systems that perform the specified functions, or combinations of special-purpose hardware and computer instructions. The flowchartillustrated by the flowchart diagram ofis a flow chart for utilizing probe data to identify a trip of vehicle which may improve traffic analytics. Fewer, more, or different steps may be provided.

702 112 102 112 114 112 112 112 3 FIG.A At, the trip datamay be received. In an embodiment, the systemmay be configured to receive the trip dataassociated with each of the plurality of data pointsof a trip segment of a trip. The trip dataincludes speed data and location data. In an example, the trip datafurther includes lane information of the vehicle associated with each of a plurality of data points and orientation information of the vehicle associated with each of a plurality of data points. Details about receiving the trip dataare provided, for example, in.

704 102 114 114 3 FIG.A At, the declaration condition may be determined. In an embodiment, the systemmay be configured to determine the deceleration condition associated with the set of consecutive data points of the plurality of data points. The set of consecutive data points terminates at an endpoint of the plurality of data points. Details about determining the deceleration conditions are provided, for example, in.

706 102 3 FIG.A At, the geographical region may be determined. In an embodiment, the systemmay be configured to determine a geographical region associated with the set of consecutive data points based on the location data of each data point of the set of consecutive data points, in response to determining the deceleration condition. Details about determining the geographical region are provided, for example, in.

708 102 3 FIG.B At, the geographical region to be associated with a point of interest (POI) location may be determined. In an embodiment, the systemmay be configured to determine the geographical region to be associated with a point of interest (POI) location. Details about determining the POI are provided, for example, in.

710 102 110 110 3 FIG.B 4 FIG. At, the probability value may be determined. In an embodiment, the systemmay be configured to determine, using a machine learning (ML) model, a probability value for occurrence of a stop event of the trip based on the determined geographical region to correspond to the POI location. Details about ML modelare provided, for example, in, and.

712 112 102 112 At, the trip datamay be stored. In an embodiment, the systemmay be configured to store the trip datain association with the stop event of the trip based on the determination of the probability value to be greater than a threshold value.

700 700 700 Accordingly, blocks of the flowchartsupport combinations of means for performing the specified functions and combinations of operations for performing the specified functions. It will also be understood that one or more blocks of the flowchart, and combinations of blocks in the flowchart, can be implemented by special-purpose hardware-based computer systems which perform the specified functions, or combinations of special-purpose hardware and computer instructions.

202 700 In some embodiments, the processormay include means for performing each of the operations as mentioned earlier in conjunction with flowchart. In this regard, according to an example embodiment, examples of means for performing operations may comprise, for example, the processor and/or a device or circuit for executing instructions or executing an algorithm for processing information as described above.

8 FIG. 8 FIG. 1 FIG. 2 FIG. 3 FIG.A 3 FIG.B 3 FIG.C 4 FIG. 5 FIG. 6 FIG. 7 FIG. 8 FIG. 8 FIG. 800 108 802 802 802 illustrates an exemplary map database record storing data, in accordance with an embodiment of the disclosure.is explained in conjunction with elements from,,,,,,,, and. With reference to, there is shown a format of the map datastored in the map databaseB according to one or more example embodiments.shows a link data recordthat may be used to store data associated with one or more of the feature lines. This link data recordmay include information (such as “attributes”, “fields”, etc.) associated with it that may allow identification of the nodes associated with the link and/or the geographic positions (e.g., the latitude and longitude coordinates and/or altitude or elevation) of the two nodes. In addition, the link data recordmay include information (e.g., more “attributes”, “fields”, etc.) that may specify the permitted speed of travel on a portion of the road may be represented by the link record, the direction of travel permitted on the road portion may be represented by the link record, if any turn restrictions may exist at each of the nodes corresponding to intersections at the ends of the road portion may be represented by the link record, the street address ranges of the roadway portion may be represented by the link record, the name of the road, and so on. The various attributes associated with a link may be included in a single data record or are included in more than one type of record which are referenced to each other.

108 802 Each link data record that may represent other-than-straight road segment may include shape point data. The shape point is a location along a link between its endpoints. To represent the shape of other-than-straight roads, the mapping platformand its associated map database developer may select one or more shape points along the other-than-straight road portion. The shape point data may be included in the link data recordindicative of the position, (e.g., latitude, longitude, and optionally, altitude or elevation) of the selected shape points along the represented link.

108 804 804 Additionally, in the compiled geographic database, such as a copy of the map databaseB, there may also be a node data recordfor each node. The node data recordmay be associated with information (such as “attributes”, “fields”, etc.) that may allow identification of the link(s) that may connect to it and/or its geographic position (e.g., its latitude, longitude, and optionally altitude or elevation).

In some embodiments, compiled geographic databases may be organized to facilitate the performance of various navigation-related functions. One way to facilitate performance of navigation-related functions may be to provide separate collections or subsets of the geographic data for use by specific navigation-related functions. Each such separate collection may include the data and attributes needed for performing the associated function but may exclude data and attributes that may not be needed for performing the function. Thus, the map data may be alternately stored in a format suitable for performing types of navigation functions, and further may be provided on-demand, depending on the type of navigation function.

9 FIG. 9 FIG. 1 FIG. 2 FIG. 3 FIG.A 3 FIG.B 3 FIG.C 4 FIG. 5 FIG. 6 FIG. 7 FIG. 8 FIG. 9 FIG. 9 FIG. 9 FIG. 900 108 900 902 902 108 902 illustrates another exemplary map database record storing data, in accordance with an embodiment of the disclosure.is explained in conjunction with elements from,,,,,,,,, and. With reference to, there is shown another format of the map datastored in the map databaseB according to one or more example embodiments. In the, the map datais stored by specifying a road segment data record. The road segment data recordis configured to represent data that represents a road network. In, the map databaseB contains at least one road segment data record(also referred to as “entity” or “entry”) for each road segment in a geographic region.

108 904 904 904 902 904 904 8 FIG. The map databaseB that represents the geographic region ofmay also include node data records(a node data recordA and a node data recordB) (or “entity” or “entry”) for each node associated with the at least one road segment shown by the road segment data record. (The terms “nodes” and “segments” represent only one terminology for describing these physical geographic features and other terminology for describing these features is intended to be encompassed within the scope of these concepts). Each of the node data recordsA andB may have associated information (such as “attributes”, “fields”, etc.) that may allow identification of the road segment(s) that may connect to it and/or its geographic position (e.g., its latitude and longitude coordinates).

9 FIG. 902 108 902 902 108 902 902 902 902 902 depicts the components of road segment data recordcontained in the map databaseB. The road segment data recordmay include a segment IDA by which the data record may be identified in the map databaseB. The segment IDA may be associated with its information (such as “attributes”, “fields”, etc.) that may describe features of the represented road segment. The road segment data recordmay include restriction direction dataB that may indicate the restrictions, if any, on the direction of vehicular travel permitted on the represented road segment. The road segment data recordmay include speed limit dataC that may indicate a static speed limit or speed category (i.e., a range indicating maximum permitted vehicular speed of travel) on the represented road segment. The static speed limit is a term used for speed limits with a permanent character, even if they are variable in a pre-determined way, such as dependent on the time of the day or weather. The static speed limit is the sign posted explicit speed limit for the road segment, or the non-sign posted implicit general speed limit based on legislation.

902 902 The road segment data recordmay include 2D geometry dataD indicative of two-dimensional (“2D”) geometry or shape of the road segment. If a road segment is straight, its shape may be represented by identifying its endpoints or nodes. However, if a road segment is other-than-straight, additional information may be required to indicate the shape of the road. One way to represent the shape of an other-than-straight road segment may be to use shape points. Shape points are points through which a road segment passes between its end points. By providing the latitude and longitude coordinates of one or more shape points, the shape of another-than-straight road segment may be represented. Another way of representing other-than-straight road segment may be with mathematical expressions, such as polynomial splines.

902 902 902 902 902 902 902 The road segment data recordmay include road grade dataE that may be indicative of the grade or slope of the road segment. In one embodiment, the road grade dataE may include road grade change points and a corresponding percentage of grade change. Additionally, the road grade dataE may include the corresponding percentage of grade change for both directions of a bi-directional road segment. The location of the road grade change point may be represented as a position along the road segment, such as thirty feet from the end or node of the road segment. For example, the road segment may have an initial road grade associated with its beginning node. The road grade change point may indicate position on the road segment wherein the road grade or slope changes, and percentage of grade change may indicate a percentage increase or decrease of the grade or slope. Each road segment may have several grade change points depending on the geometry of the road segment. In another embodiment, the road grade dataE may include the road grade change points and an actual road grade value for the portion of the road segment after the road grade change point until the next road grade change point or end node. In an embodiment, the road grade dataE may include elevation data at the road grade change points and nodes. In an alternative embodiment, the road grade dataE may be an elevation model which may be used to determine the slope of the road segment.

902 902 902 The road segment data recordmay include or be associated with other dataF that may refer to various other attributes of the represented road segment. The various attributes associated with a road segment may be included in a single road segment record or may be included in more than one type of record which cross-reference of each other. For example, the road segment data recordmay include data identifying the name or names by which the represented road segment is known, the street address ranges along the represented road segment, and or the like.

902 902 902 904 The road segment data recordmay include endpointsG providing the geographic coordinates (e.g., the latitude and longitude) of the end points of the represented road segment. In one embodiment, the endpointsG may be references to the node data recordsthat may represent the nodes corresponding to the end points of the represented road segment.

9 FIG. 9 FIG. 904 108 904 904 904 904 904 1 904 1 904 904 904 904 2 904 2 may represent components of the node data recordscontained in the map databaseB. Each of the node data recordsmay include associated information (such as “attributes”, “fields”, etc.) that may allow identification of the road segment(s) that may connect to it and/or it is geographic position (e.g., its latitude and longitude coordinates). For the embodiment shown in, the node data recordsincludingA andB that may include the latitude and longitude coordinatesAandBfor their nodes accordingly. The node data recordsincludingA andB may also include other dataAandBthat may refer to various other attributes of the nodes.

108 108 108 8 FIG. 9 FIG. Thus, the overall data stored in the map databaseB may be organized in the form of different layers for greater detail, clarity, and precision. Specifically, in the case of high-definition maps, the map data may be organized, stored, sorted, and accessed in the form of three or more layers. The layers may include road level layer, lane level layer and localization layer. The data stored in the map databaseB may be stored in the formats shown inand. The data stored in the map databaseB may be combined in a suitable manner to provide these three or more layers of information. In an embodiment, there may be lesser or fewer number of layers of data possible, without deviating from the scope of the present disclosure.

10 FIG. 1000 108 108 1010 illustrates another block diagramof the map databaseB storing data, in accordance with an embodiment of the disclosure. The map databaseB may store map data or geographic datain the form of road segments/links, nodes, and one or more associated attributes as discussed above. Furthermore, attributes may refer to features or data layers associated with the link-node database, such as an HD lane data layer.

1010 1006 1006 1006 108 1008 1008 108 1002 902 1004 904 9 FIG. 9 FIG. In addition, the geographic datamay include other kinds of data. The other kinds of datamay represent other kinds of geographic features. The other kinds of datamay include point of interest data. For example, the point of interest data may include point of interest records comprising a type (e.g., the type of point of interest, such as restaurant, ATM, etc.), location of the point of interest, a phone number, hours of operation, etc. The map databaseB may include indexes. The indexesmay include various types of indexes that may relate with the different types of data to each other or may relate to other aspects of the data contained in the map databaseB. The road segment data recordsis an exemplary embodiment of the road segment data recordof. The node data recordsis an exemplary embodiment of the node data recordsof.

108 102 108 108 108 8 FIG. 9 FIG. 10 FIG. The data stored in the map databaseB in the various formats discussed above may help in providing precise data for high-definition mapping applications, autonomous vehicle navigation and guidance, cruise control using ADAS, direction control using accurate vehicle maneuvering and other such services. In an embodiment, the systemmay be configured to access the map databaseB. The map databaseB may store data in the form of various layers and formats depicted in,and. The map databaseB may additionally store the image data and the optimized model used for inference deduction that may be accessed by the user device for faster processing.

Many modifications and other embodiments of the inventions set forth herein will come to mind to one skilled in the art to which these inventions pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the inventions are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Moreover, although the foregoing descriptions and the associated drawings describe example embodiments in the context of certain example combinations of reactants and/or functions, it should be appreciated that different combinations of reactants and/or functions may be provided by alternative embodiments without departing from the scope of the appended claims. In this regard, for example, different combinations of reactants and/or functions than those explicitly described above are also contemplated as may be set forth in some of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.